


'''simple docstring'''


import os
from argparse import ArgumentParser
from typing import List

import torch.utils.data

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node


UpperCAmelCase_						:  Dict				  =						4
UpperCAmelCase_						:  Any				  =						3





class        lowercase__    (		_snake_case   ):





							'''simple docstring'''


							pass







def      snake_case_  (    SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				for shard in shards:
								for i in range(SCREAMING_SNAKE_CASE__	):
												yield {"i": i, "shard": shard}







def      snake_case_  (    ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			Dict							= int(os.environ["""RANK"""]	)
				_SCREAMING_SNAKE_CASE					:			int							= int(os.environ["""WORLD_SIZE"""]	)

				_SCREAMING_SNAKE_CASE					:			List[Any]							= ArgumentParser()
				parser.add_argument("""--streaming"""				, type=SCREAMING_SNAKE_CASE__	)
				parser.add_argument("""--local_rank"""				, type=SCREAMING_SNAKE_CASE__	)
				parser.add_argument("""--num_workers"""				, type=SCREAMING_SNAKE_CASE__				, default=0	)
				_SCREAMING_SNAKE_CASE					:			int							= parser.parse_args()
				_SCREAMING_SNAKE_CASE					:			Optional[Any]							= args.streaming
				_SCREAMING_SNAKE_CASE					:			List[Any]							= args.num_workers

				_SCREAMING_SNAKE_CASE					:			Dict							= {"""shards""": [f"""shard_{shard_idx}""" for shard_idx in range(SCREAMING_SNAKE_CASE__	)]}
				_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= IterableDataset.from_generator(SCREAMING_SNAKE_CASE__				, gen_kwargs=SCREAMING_SNAKE_CASE__	)
				if not streaming:
								_SCREAMING_SNAKE_CASE					:			Optional[int]							= Dataset.from_list(list(SCREAMING_SNAKE_CASE__	)	)

				_SCREAMING_SNAKE_CASE					:			List[Any]							= split_dataset_by_node(SCREAMING_SNAKE_CASE__				, rank=SCREAMING_SNAKE_CASE__				, world_size=SCREAMING_SNAKE_CASE__	)
				_SCREAMING_SNAKE_CASE					:			Dict							= torch.utils.data.DataLoader(SCREAMING_SNAKE_CASE__				, num_workers=SCREAMING_SNAKE_CASE__	)

				_SCREAMING_SNAKE_CASE					:			str							= NUM_SHARDS * NUM_ITEMS_PER_SHARD
				_SCREAMING_SNAKE_CASE					:			List[str]							= full_size // world_size
				expected_local_size += int(rank < (full_size % world_size)	)

				_SCREAMING_SNAKE_CASE					:			List[Any]							= sum(1 for _ in dataloader	)
				if local_size != expected_local_size:
								raise FailedTestError(f"""local_size {local_size} != expected_local_size {expected_local_size}"""	)


if __name__ == "__main__":
				main()








'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase_						:  Dict				  =						{
    'configuration_mobilebert': [
        'MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'MobileBertConfig',
        'MobileBertOnnxConfig',
    ],
    'tokenization_mobilebert': ['MobileBertTokenizer'],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase_						:  Optional[Any]				  =						['MobileBertTokenizerFast']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase_						:  Optional[Any]				  =						[
				    'MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'MobileBertForMaskedLM',
				    'MobileBertForMultipleChoice',
				    'MobileBertForNextSentencePrediction',
				    'MobileBertForPreTraining',
				    'MobileBertForQuestionAnswering',
				    'MobileBertForSequenceClassification',
				    'MobileBertForTokenClassification',
				    'MobileBertLayer',
				    'MobileBertModel',
				    'MobileBertPreTrainedModel',
				    'load_tf_weights_in_mobilebert',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase_						:  List[str]				  =						[
				    'TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'TFMobileBertForMaskedLM',
				    'TFMobileBertForMultipleChoice',
				    'TFMobileBertForNextSentencePrediction',
				    'TFMobileBertForPreTraining',
				    'TFMobileBertForQuestionAnswering',
				    'TFMobileBertForSequenceClassification',
				    'TFMobileBertForTokenClassification',
				    'TFMobileBertMainLayer',
				    'TFMobileBertModel',
				    'TFMobileBertPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_mobilebert import (
				    MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    MobileBertConfig,
				    MobileBertOnnxConfig,
				)
				from .tokenization_mobilebert import MobileBertTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_mobilebert_fast import MobileBertTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_mobilebert import (
								    MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    MobileBertForMaskedLM,
								    MobileBertForMultipleChoice,
								    MobileBertForNextSentencePrediction,
								    MobileBertForPreTraining,
								    MobileBertForQuestionAnswering,
								    MobileBertForSequenceClassification,
								    MobileBertForTokenClassification,
								    MobileBertLayer,
								    MobileBertModel,
								    MobileBertPreTrainedModel,
								    load_tf_weights_in_mobilebert,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_mobilebert import (
								    TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFMobileBertForMaskedLM,
								    TFMobileBertForMultipleChoice,
								    TFMobileBertForNextSentencePrediction,
								    TFMobileBertForPreTraining,
								    TFMobileBertForQuestionAnswering,
								    TFMobileBertForSequenceClassification,
								    TFMobileBertForTokenClassification,
								    TFMobileBertMainLayer,
								    TFMobileBertModel,
								    TFMobileBertPreTrainedModel,
								)

else:
				import sys

				UpperCAmelCase_						:  str				  =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




